Population in Core County

Ranking

Trendline

map_jc_00 = readOGR(‘data/maps/tract2000’, layer = “tr21_d00”, GDAL1_integer64_policy = TRUE, stringsAsFactors = FALSE, verbose = FALSE)

map_jc_00\(TRACT <- as.numeric(map_jc_00\)TRACT)

Pop

Change in Population 2008-2014

Percent Change

This map shows the change in the percentage of residents for each census tract. Data are five-year averages from 2008 and 2014. 

##   [1] 4.075104 4.000892 3.985371 4.141890 4.161778 4.204630 4.214976
##   [8] 4.108160 4.142172 4.195136 4.102059 4.360335 4.373332 4.053844
##  [15] 4.046785 4.106525 4.121119 5.142077 4.704795 4.278122 4.130230
##  [22] 4.120311 4.300300 4.193726 4.420506 4.541269 4.337842 4.736963
##  [29] 4.173651 4.133581 4.354862 4.369219 4.192671 4.395541 4.389152
##  [36] 3.941395 3.735355 4.218926 4.178198 4.148184 3.993816 3.955275
##  [43] 4.191756 5.128710 3.770894 3.904811 3.896253 3.865138 4.162810
##  [50] 3.828271 4.121950 5.488429       NA 4.210695 4.169634 4.140003
##  [57] 4.129598 4.201265 4.244187 4.201463 4.248635 4.296525 4.091932
##  [64] 4.388074 4.180676 4.195719 4.205068 4.057348 4.243907 4.270836
##  [71] 4.072411 4.231113 4.059936 4.129124 4.305287 4.160617 4.110324
##  [78] 4.430158 4.299407 5.898710 4.528342 4.703251 4.656774 4.906375
##  [85] 5.250997 5.002339 4.471419 4.463386 4.630192 4.455917 4.516828
##  [92] 4.317960 3.981264 3.986908 4.069107 4.184054 4.221752 2.009187
##  [99] 4.150458 4.160784 4.696115 4.209546 4.073380 4.403895 4.037474
## [106] 4.136164 3.496196 3.888830 4.162168 3.673765 4.334768 4.178714
## [113] 4.179374 4.388373 4.283187 4.324865 4.202705 4.130446 4.107781
## [120] 4.349890 4.650194 4.579322 4.798220 4.275132 4.342681 4.446496
## [127] 4.574243 4.534185 4.680542 5.186421 5.301264 5.186300 4.723853
## [134] 4.761705 4.934424 5.510155 5.444242 4.475147 4.403918 3.647998
## [141] 0.000000 4.399022 4.115843 4.352099 4.159922 4.351904 4.036591
## [148] 4.176806 4.392168 4.494167 4.168438 4.357128 4.068170 4.327258
## [155] 4.373884 4.601587 4.117539 4.134790 4.544618 4.263086 4.392097
## [162] 4.176892 4.307467 4.471645 4.419702 4.180165 4.315309 4.249905
## [169] 4.238440 4.218360 4.309735 4.117637 4.253096 4.596219 4.126434
## [176] 4.191331 4.317557 4.291697 4.495656 4.494745 4.367868 4.095351
## [183] 4.354373 4.402156 3.787806 4.030746 4.307206 4.015683 4.126660
## [190]      NaN 4.114165

va = “population_change” name = “” units = “Percent” map_style = “divergent” legend_title = “” palette = “” reverse = F

#renames var for use with the ’\(' operator map_jc@data\)var <- map_jc@data[[“population_change”]]

#concatenate third line of text for tract labels using units parameter if(units == “Percent”){ map_jc@data$l_line3 <- paste(name, “:”, round(map_jc@data$var, 2),“%”, sep = “”) } if(units == “Dollars”){ map_jc@data$l_line3 <- paste(name, “: \(", prettyNum( signif(map_jc@data\)var, 3), big.mark =”,“, preserve.width =”none" ), sep = “”) } if(units == “minutes”){ map_jc@data$l_line3 <- paste(name, “:”, round(map_jc@data$var, 2)," minutes“, sep =”“) } if(units ==”people“){ map_jc@data$l_line3 <- paste(name,”: “, round(map_jc@data$var, 2),” people“, sep =”“) } if(units ==”none“){ map_jc@data$l_line3 <- paste(name,”: “, round(map_jc@data$var, 2), sep =”“) }

#combine lines of text into full formatted label labels <- sprintf(“%s
%s
%s”, map_jc@data$l_line1, map_jc@data$l_line2, map_jc@data$l_line3 ) %>% lapply(htmltools::HTML)

labels[[190]] <- htmltools::HTML(sprintf(“%s
%s
%s”, “Tract #: 980000”, “Louisville International Airport”, “No residents” ) )

#Define palette using map_style parameter if(map_style == “sequential” | map_style == “Sequential”){ col_palette = “BuPu” } if(map_style == “divergent” | map_style == “Divergent”){ col_palette = “RdYlGn” }

#pal <- brewer.pal(11, col_palette) neg <- colorNumeric( palette = “OrRd”, domain = subset(map_jc@data, var <= 0)\(var ) pos <- colorNumeric( palette = "Greens", domain = subset(map_jc@data, var > 0)\)var )

pal <- colorNumeric( palette = “Greens”, domain = map_jc@data$var )

pal <- function(x){ this <- pos(x) that <- neg(x)

color <- if_else(x > 0, this, that)
color <- replace_na(color, "#808080")
color

}

#Create map title using legend_title parameter if(units == “Percent”) { title_text <- paste(legend_title, “(%)”, sep = ‘’) } if(units == “Dollars”) { title_text <- paste(legend_title, “($)”, sep = ‘’) } if(units == “minutes”){ title_text <- paste(legend_title, “(minutes)”, sep = ‘’) } if(units == “people”){ title_text <- paste(legend_title, “(people)”, sep = ‘’) } if(units == “none”){ title_text <- legend_title }

#create map m <- leaflet(map_jc) %>% addTiles() %>% addPolygons(color = “#444444”, weight = 1, smoothFactor = 0.5, opacity = 1.0, fillOpacity = 0.5, fillColor = ~pal(var), label = labels, labelOptions = labelOptions( style = list(“font-weight” = “normal”, padding = “3px 8px”), textsize = “15px”, direction = “auto”)) %>% addLegend(pal = pal, values = ~var, opacity = 0.7, title = title_text, position = “bottomright”) m pal2(2000)

Adjusted Change

This map shows the percentage change in each county’s population relative to the change in the entire county’s population.

Percent Change 2

Adjusted Change 2

Homeownership

Ranking

Trendline

Overall

By Ethnicity

Gap

Map

Homeownership

Burdened Households

observe({ var <- input$variable

leafletProxy("map", data = map_data) %>%
  addTiles() %>%
  addPolygons(color = "#444444", weight = 1, smoothFactor = 0.5,
              opacity = 1.0, fillOpacity = 0.5,
              fillColor = ~pal(var),
              #label = labels,
              labelOptions = labelOptions(
              style = list("font-weight" = "normal", padding = "3px 8px"),
              textsize = "15px",
              direction = "auto"))%>%
  addLegend(pal = pal, values = ~var, opacity = 0.7, title = var,
         position = "bottomright")

})

Change in Home Values by Zip

2000 to 2017

2013 to 2017

2017

Segregation

Numbers do not add to 100% due to rounding.
Percent of City in Tract Percent of Black Residents in Tract Percent of Tract that is Black Cumulative % of Black Residents Cumulative % of All Residents
1.0 3.3 72.2 3.3 1.0
0.7 3.1 94.7 6.4 1.7
0.9 3.0 65.8 9.4 2.6
0.7 2.5 76.0 11.9 3.3
0.4 1.9 95.2 13.8 3.7
0.4 1.9 95.7 15.7 4.1
0.5 1.9 85.8 17.6 4.6
0.8 1.9 51.7 19.5 5.4
0.4 1.8 95.2 21.3 5.8
0.7 1.8 51.0 23.1 6.5
0.4 1.7 99.0 24.8 6.9
0.7 1.7 49.1 26.5 7.6
0.4 1.5 89.3 28.0 8.0
0.6 1.5 52.0 29.5 8.6
0.6 1.5 50.2 31.0 9.2
0.3 1.4 97.0 32.4 9.5
0.3 1.4 84.1 33.8 9.8
0.4 1.4 83.9 35.2 10.2
0.7 1.4 38.5 36.6 10.9
0.7 1.4 39.9 38.0 11.6
0.4 1.4 76.8 39.4 12.0
0.3 1.3 88.2 40.7 12.3
0.3 1.3 94.3 42.0 12.6
0.8 1.3 35.1 43.3 13.4
0.3 1.2 95.8 44.5 13.7
0.2 1.1 93.8 45.6 13.9
0.5 1.1 45.6 46.7 14.4
0.4 1.1 50.6 47.8 14.8
1.0 1.1 21.9 48.9 15.8
0.4 1.1 53.7 50.0 16.2
0.7 1.0 31.3 51.0 16.9
0.6 1.0 32.5 52.0 17.5
0.9 1.0 23.0 53.0 18.4
0.4 1.0 54.6 54.0 18.8
0.3 1.0 67.4 55.0 19.1
0.2 0.9 95.6 55.9 19.3
0.3 0.9 75.0 56.8 19.6
0.5 0.9 37.9 57.7 20.1
0.9 0.9 20.8 58.6 21.0
1.0 0.9 18.9 59.5 22.0
0.7 0.8 21.2 60.3 22.7
0.3 0.8 53.0 61.1 23.0
0.5 0.7 27.3 61.8 23.5
0.2 0.7 65.1 62.5 23.7
0.9 0.7 17.9 63.2 24.6
0.4 0.7 33.2 63.9 25.0
0.7 0.7 21.8 64.6 25.7
0.3 0.6 40.7 65.2 26.0
0.3 0.6 34.6 65.8 26.3
0.3 0.6 47.2 66.4 26.6
0.4 0.6 32.9 67.0 27.0
0.5 0.6 29.0 67.6 27.5
0.9 0.6 12.5 68.2 28.4
0.7 0.6 18.0 68.8 29.1
0.8 0.6 15.0 69.4 29.9
0.4 0.6 29.9 70.0 30.3
0.7 0.6 17.4 70.6 31.0
0.4 0.5 27.4 71.1 31.4
0.3 0.5 29.0 71.6 31.7
0.5 0.5 20.8 72.1 32.2
0.5 0.5 21.1 72.6 32.7
0.9 0.5 11.0 73.1 33.6
0.9 0.5 12.2 73.6 34.5
0.6 0.5 18.6 74.1 35.1
0.5 0.5 21.3 74.6 35.6
0.5 0.5 18.5 75.1 36.1
0.5 0.5 19.9 75.6 36.6
0.8 0.5 14.1 76.1 37.4
0.2 0.4 34.4 76.5 37.6
0.4 0.4 25.5 76.9 38.0
0.5 0.4 17.0 77.3 38.5
0.4 0.4 17.9 77.7 38.9
0.4 0.4 20.3 78.1 39.3
0.6 0.4 11.9 78.5 39.9
0.6 0.4 13.7 78.9 40.5
0.6 0.4 13.6 79.3 41.1
0.8 0.4 10.4 79.7 41.9
0.7 0.4 10.7 80.1 42.6
0.8 0.4 11.5 80.5 43.4
0.7 0.4 12.9 80.9 44.1
0.8 0.4 11.8 81.3 44.9
0.7 0.4 11.7 81.7 45.6
0.7 0.4 11.4 82.1 46.3
0.7 0.4 11.2 82.5 47.0
0.3 0.4 22.5 82.9 47.3
1.0 0.4 9.4 83.3 48.3
1.1 0.4 6.9 83.7 49.4
0.8 0.4 9.1 84.1 50.2
0.3 0.4 24.1 84.5 50.5
0.7 0.4 12.0 84.9 51.2
0.2 0.3 24.4 85.2 51.4
0.2 0.3 30.5 85.5 51.6
0.4 0.3 16.2 85.8 52.0
0.3 0.3 19.2 86.1 52.3
0.4 0.3 17.0 86.4 52.7
0.5 0.3 13.2 86.7 53.2
0.5 0.3 12.5 87.0 53.7
0.9 0.3 7.9 87.3 54.6
0.6 0.3 9.8 87.6 55.2
0.9 0.3 7.0 87.9 56.1
0.8 0.3 7.0 88.2 56.9
0.6 0.3 10.1 88.5 57.5
0.8 0.3 8.8 88.8 58.3
0.4 0.3 16.9 89.1 58.7
0.3 0.3 17.4 89.4 59.0
0.5 0.3 12.1 89.7 59.5
0.6 0.3 10.2 90.0 60.1
0.5 0.3 11.2 90.3 60.6
0.5 0.3 14.0 90.6 61.1
0.5 0.3 11.2 90.9 61.6
0.5 0.2 8.2 91.1 62.1
0.6 0.2 7.8 91.3 62.7
0.2 0.2 19.4 91.5 62.9
0.9 0.2 5.1 91.7 63.8
0.6 0.2 7.6 91.9 64.4
0.5 0.2 5.8 92.1 64.9
0.6 0.2 7.4 92.3 65.5
0.4 0.2 11.4 92.5 65.9
0.6 0.2 7.4 92.7 66.5
0.6 0.2 5.3 92.9 67.1
0.5 0.2 9.0 93.1 67.6
0.6 0.2 6.7 93.3 68.2
0.5 0.2 8.8 93.5 68.7
0.5 0.2 10.3 93.7 69.2
0.8 0.2 5.2 93.9 70.0
0.8 0.2 5.8 94.1 70.8
0.3 0.2 13.2 94.3 71.1
0.4 0.2 11.5 94.5 71.5
0.9 0.2 6.0 94.7 72.4
0.4 0.2 11.4 94.9 72.8
0.6 0.2 8.2 95.1 73.4
0.4 0.2 9.8 95.3 73.8
0.3 0.2 10.7 95.5 74.1
0.6 0.2 6.6 95.7 74.7
0.5 0.2 8.4 95.9 75.2
0.3 0.2 15.7 96.1 75.5
0.3 0.2 13.1 96.3 75.8
0.2 0.1 8.1 96.4 76.0
0.2 0.1 5.0 96.5 76.2
0.2 0.1 11.7 96.6 76.4
0.3 0.1 4.5 96.7 76.7
0.3 0.1 9.9 96.8 77.0
0.3 0.1 4.2 96.9 77.3
0.7 0.1 3.9 97.0 78.0
0.3 0.1 11.1 97.1 78.3
0.5 0.1 5.7 97.2 78.8
0.4 0.1 6.0 97.3 79.2
0.4 0.1 5.1 97.4 79.6
0.6 0.1 3.2 97.5 80.2
0.4 0.1 2.9 97.6 80.6
0.6 0.1 5.0 97.7 81.2
0.5 0.1 2.8 97.8 81.7
0.4 0.1 3.1 97.9 82.1
0.4 0.1 2.5 98.0 82.5
0.6 0.1 2.3 98.1 83.1
0.8 0.1 1.6 98.2 83.9
0.5 0.1 4.4 98.3 84.4
0.4 0.1 5.2 98.4 84.8
0.6 0.1 3.3 98.5 85.4
0.5 0.1 4.9 98.6 85.9
0.6 0.1 4.7 98.7 86.5
0.8 0.1 3.9 98.8 87.3
0.5 0.1 3.9 98.9 87.8
0.6 0.1 3.8 99.0 88.4
0.7 0.1 1.5 99.1 89.1
0.7 0.1 2.9 99.2 89.8
0.4 0.1 4.3 99.3 90.2
0.3 0.0 1.0 99.3 90.5
0.7 0.0 0.6 99.3 91.2
0.3 0.0 2.4 99.3 91.5
0.3 0.0 1.5 99.3 91.8
0.4 0.0 1.3 99.3 92.2
0.3 0.0 2.9 99.3 92.5
0.6 0.0 1.5 99.3 93.1
0.6 0.0 1.2 99.3 93.7
0.6 0.0 0.0 99.3 94.3
0.4 0.0 2.1 99.3 94.7
0.3 0.0 0.1 99.3 95.0
0.4 0.0 2.4 99.3 95.4
0.6 0.0 1.5 99.3 96.0
0.7 0.0 0.7 99.3 96.7
0.4 0.0 2.2 99.3 97.1
0.2 0.0 0.6 99.3 97.3
0.3 0.0 2.9 99.3 97.6
0.5 0.0 1.3 99.3 98.1
0.4 0.0 0.3 99.3 98.5
0.1 0.0 0.0 99.3 98.6
0.4 0.0 1.2 99.3 99.0
0.4 0.0 2.5 99.3 99.4
0.3 0.0 0.5 99.3 99.7
0.0 0.0 NaN 99.3 99.7

Commute Time

Average Commute

Extreme Commutes

1.5+ hours

2+ hours

3+ hours

Brain Drain

Net Migration

This graph shows single-year estimates.